Plot the fitted values from a DFA

plot_fitted(
  modelfit,
  conf_level = 0.95,
  names = NULL,
  spaghetti = FALSE,
  time_labels = NULL
)

Arguments

modelfit

Output from fit_dfa, a rstanfit object

conf_level

Probability level for CI.

names

Optional vector of names for plotting labels TODO. Should be same length as the number of time series

spaghetti

Defaults to FALSE, but if TRUE puts all raw time series (grey) and fitted values on a single plot

time_labels

Optional vector of time labels for plotting, same length as number of time steps

See also

plot_loadings fit_dfa rotate_trends dfa_fitted

Examples

# \donttest{ y <- sim_dfa(num_trends = 2, num_years = 20, num_ts = 4) m <- fit_dfa(y = y$y_sim, num_trends = 2, iter = 50, chains = 1)
#> #> SAMPLING FOR MODEL 'dfa' NOW (CHAIN 1). #> Chain 1: #> Chain 1: Gradient evaluation took 4e-05 seconds #> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.4 seconds. #> Chain 1: Adjust your expectations accordingly! #> Chain 1: #> Chain 1: #> Chain 1: WARNING: There aren't enough warmup iterations to fit the #> Chain 1: three stages of adaptation as currently configured. #> Chain 1: Reducing each adaptation stage to 15%/75%/10% of #> Chain 1: the given number of warmup iterations: #> Chain 1: init_buffer = 3 #> Chain 1: adapt_window = 20 #> Chain 1: term_buffer = 2 #> Chain 1: #> Chain 1: Iteration: 1 / 50 [ 2%] (Warmup) #> Chain 1: Iteration: 5 / 50 [ 10%] (Warmup) #> Chain 1: Iteration: 10 / 50 [ 20%] (Warmup) #> Chain 1: Iteration: 15 / 50 [ 30%] (Warmup) #> Chain 1: Iteration: 20 / 50 [ 40%] (Warmup) #> Chain 1: Iteration: 25 / 50 [ 50%] (Warmup) #> Chain 1: Iteration: 26 / 50 [ 52%] (Sampling) #> Chain 1: Iteration: 30 / 50 [ 60%] (Sampling) #> Chain 1: Iteration: 35 / 50 [ 70%] (Sampling) #> Chain 1: Iteration: 40 / 50 [ 80%] (Sampling) #> Chain 1: Iteration: 45 / 50 [ 90%] (Sampling) #> Chain 1: Iteration: 50 / 50 [100%] (Sampling) #> Chain 1: #> Chain 1: Elapsed Time: 0.005645 seconds (Warm-up) #> Chain 1: 0.023988 seconds (Sampling) #> Chain 1: 0.029633 seconds (Total) #> Chain 1:
#> Warning: There were 19 divergent transitions after warmup. See #> http://mc-stan.org/misc/warnings.html#divergent-transitions-after-warmup #> to find out why this is a problem and how to eliminate them.
#> Warning: There were 1 chains where the estimated Bayesian Fraction of Missing Information was low. See #> http://mc-stan.org/misc/warnings.html#bfmi-low
#> Warning: Examine the pairs() plot to diagnose sampling problems
#> Warning: The largest R-hat is NA, indicating chains have not mixed. #> Running the chains for more iterations may help. See #> http://mc-stan.org/misc/warnings.html#r-hat
#> Warning: Bulk Effective Samples Size (ESS) is too low, indicating posterior means and medians may be unreliable. #> Running the chains for more iterations may help. See #> http://mc-stan.org/misc/warnings.html#bulk-ess
#> Warning: Tail Effective Samples Size (ESS) is too low, indicating posterior variances and tail quantiles may be unreliable. #> Running the chains for more iterations may help. See #> http://mc-stan.org/misc/warnings.html#tail-ess
#> Inference for the input samples (1 chains: each with iter = 25; warmup = 12): #> #> Q5 Q50 Q95 Mean SD Rhat Bulk_ESS Tail_ESS #> x[1,1] -0.6 -0.6 -0.6 -0.6 0.0 1.03 13 13 #> x[2,1] 0.4 0.4 0.4 0.4 0.0 1.16 9 13 #> x[1,2] 1.0 1.0 1.0 1.0 0.0 1.16 9 13 #> x[2,2] -0.7 -0.6 -0.6 -0.6 0.0 1.11 8 13 #> x[1,3] 1.7 1.7 1.7 1.7 0.0 2.16 3 6 #> x[2,3] -0.2 -0.1 -0.1 -0.1 0.0 1.16 7 13 #> x[1,4] 1.6 1.6 1.6 1.6 0.0 2.16 3 6 #> x[2,4] -0.4 -0.4 -0.4 -0.4 0.0 0.99 9 13 #> x[1,5] 2.8 2.8 2.8 2.8 0.0 2.16 3 6 #> x[2,5] -1.0 -1.0 -1.0 -1.0 0.0 1.16 10 13 #> x[1,6] 1.0 1.1 1.1 1.1 0.0 2.16 3 6 #> x[2,6] -1.6 -1.6 -1.6 -1.6 0.0 2.23 9 13 #> x[1,7] 2.0 2.1 2.1 2.1 0.0 2.16 3 6 #> x[2,7] -0.6 -0.6 -0.6 -0.6 0.0 1.63 10 13 #> x[1,8] 1.9 1.9 1.9 1.9 0.0 2.16 3 6 #> x[2,8] 0.3 0.3 0.3 0.3 0.0 1.63 10 13 #> x[1,9] 2.4 2.5 2.5 2.5 0.0 2.16 3 6 #> x[2,9] -1.3 -1.3 -1.3 -1.3 0.0 2.22 9 13 #> x[1,10] 2.9 2.9 2.9 2.9 0.0 2.16 3 6 #> x[2,10] -0.3 -0.3 -0.2 -0.3 0.0 1.63 9 13 #> x[1,11] 2.3 2.4 2.4 2.4 0.0 2.16 3 6 #> x[2,11] -0.9 -0.9 -0.8 -0.9 0.0 2.22 9 13 #> x[1,12] 1.1 1.2 1.2 1.2 0.0 2.16 3 6 #> x[2,12] -0.7 -0.7 -0.6 -0.7 0.0 2.22 9 13 #> x[1,13] 2.1 2.2 2.2 2.2 0.0 2.16 3 6 #> x[2,13] 1.0 1.1 1.1 1.1 0.0 2.22 10 13 #> x[1,14] 3.2 3.3 3.3 3.3 0.0 2.16 3 6 #> x[2,14] -0.8 -0.8 -0.8 -0.8 0.0 1.63 10 13 #> x[1,15] 1.9 2.0 2.0 2.0 0.0 2.16 3 6 #> x[2,15] 0.9 1.0 1.0 1.0 0.0 2.22 9 13 #> x[1,16] 0.1 0.2 0.2 0.2 0.0 2.16 3 6 #> x[2,16] 2.1 2.1 2.1 2.1 0.0 1.63 13 13 #> x[1,17] -0.7 -0.6 -0.6 -0.6 0.0 2.16 3 6 #> x[2,17] 1.2 1.3 1.3 1.3 0.0 2.22 9 13 #> x[1,18] -0.9 -0.9 -0.8 -0.9 0.0 2.16 3 6 #> x[2,18] 0.4 0.4 0.4 0.4 0.0 1.14 12 13 #> x[1,19] 0.5 0.5 0.5 0.5 0.0 2.16 3 6 #> x[2,19] -0.7 -0.6 -0.6 -0.6 0.0 1.03 13 13 #> x[1,20] -0.5 -0.4 -0.4 -0.5 0.0 2.16 3 6 #> x[2,20] 0.8 0.8 0.8 0.8 0.0 1.21 12 13 #> Z[1,1] -0.3 -0.2 -0.1 -0.2 0.1 1.60 4 13 #> Z[2,1] 0.0 0.1 0.1 0.1 0.0 0.92 13 13 #> Z[3,1] -0.2 -0.1 0.2 -0.1 0.1 0.91 13 13 #> Z[4,1] -0.2 0.2 0.3 0.1 0.2 1.35 7 13 #> Z[1,2] 0.0 0.0 0.0 0.0 0.0 1.00 13 13 #> Z[2,2] 0.1 0.3 0.6 0.3 0.2 1.50 4 13 #> Z[3,2] 0.2 0.4 0.5 0.3 0.1 1.18 13 13 #> Z[4,2] -0.4 -0.2 -0.1 -0.2 0.1 1.05 8 13 #> log_lik[1] -1.0 -1.0 -0.9 -1.0 0.0 2.13 4 13 #> log_lik[2] -2.5 -2.2 -2.1 -2.2 0.1 1.20 5 13 #> log_lik[3] -1.9 -1.6 -1.5 -1.7 0.1 0.95 8 6 #> log_lik[4] -1.2 -1.1 -1.0 -1.1 0.1 1.18 9 13 #> log_lik[5] -0.8 -0.8 -0.8 -0.8 0.0 2.12 4 13 #> log_lik[6] -1.5 -1.2 -1.1 -1.2 0.1 1.20 5 13 #> log_lik[7] -1.0 -0.9 -0.8 -0.9 0.1 0.95 10 6 #> log_lik[8] -1.0 -0.9 -0.8 -0.9 0.1 1.65 9 6 #> log_lik[9] -1.0 -0.9 -0.9 -0.9 0.1 1.60 4 13 #> log_lik[10] -2.2 -2.0 -1.9 -2.0 0.1 0.92 13 13 #> log_lik[11] -1.6 -1.1 -1.0 -1.2 0.2 0.91 13 13 #> log_lik[12] -0.9 -0.9 -0.8 -0.9 0.0 0.93 13 13 #> log_lik[13] -1.6 -1.5 -1.2 -1.5 0.1 1.60 4 13 #> log_lik[14] -1.6 -1.5 -1.3 -1.5 0.1 1.39 5 13 #> log_lik[15] -1.9 -1.4 -1.2 -1.5 0.3 0.96 11 13 #> log_lik[16] -1.8 -1.2 -1.1 -1.3 0.3 1.16 9 6 #> log_lik[17] -1.0 -0.9 -0.9 -1.0 0.1 1.62 4 13 #> log_lik[18] -0.9 -0.9 -0.8 -0.9 0.0 1.63 12 13 #> log_lik[19] -1.5 -0.9 -0.8 -1.0 0.3 0.96 11 13 #> log_lik[20] -1.8 -1.0 -0.8 -1.1 0.4 1.35 9 6 #> log_lik[21] -0.9 -0.9 -0.8 -0.9 0.0 1.60 4 13 #> log_lik[22] -2.0 -1.3 -1.0 -1.3 0.4 1.26 5 13 #> log_lik[23] -1.2 -1.0 -0.8 -1.0 0.1 0.95 10 13 #> log_lik[24] -0.9 -0.8 -0.8 -0.8 0.0 1.02 9 4 #> log_lik[25] -1.7 -1.6 -1.2 -1.6 0.2 1.60 4 13 #> log_lik[26] -2.3 -2.1 -1.6 -2.1 0.2 1.19 7 13 #> log_lik[27] -2.8 -1.8 -1.5 -2.0 0.4 0.96 11 13 #> log_lik[28] -1.0 -0.8 -0.8 -0.9 0.1 1.35 4 13 #> log_lik[29] -1.2 -1.1 -1.0 -1.1 0.1 1.60 4 13 #> log_lik[30] -1.9 -1.7 -1.6 -1.7 0.1 1.49 13 13 #> log_lik[31] -2.7 -1.8 -1.5 -1.9 0.4 0.91 13 13 #> log_lik[32] -1.4 -0.8 -0.8 -1.0 0.2 1.35 7 13 #> log_lik[33] -1.2 -1.1 -0.9 -1.1 0.1 1.60 4 13 #> log_lik[34] -1.2 -0.9 -0.8 -0.9 0.1 1.20 6 13 #> log_lik[35] -1.1 -0.9 -0.8 -0.9 0.1 1.50 10 6 #> log_lik[36] -1.3 -0.9 -0.8 -1.0 0.2 1.35 9 6 #> log_lik[37] -1.1 -0.8 -0.8 -0.9 0.1 2.13 4 13 #> log_lik[38] -0.9 -0.9 -0.8 -0.9 0.0 1.01 13 13 #> log_lik[39] -1.9 -1.5 -0.9 -1.4 0.3 0.91 13 13 #> log_lik[40] -1.4 -1.2 -0.8 -1.1 0.2 1.16 11 13 #> log_lik[41] -2.3 -1.7 -1.6 -1.8 0.3 1.60 4 13 #> log_lik[42] -0.9 -0.8 -0.8 -0.9 0.0 1.79 12 13 #> log_lik[43] -1.2 -1.0 -0.8 -0.9 0.1 1.26 13 6 #> log_lik[44] -1.5 -1.2 -0.8 -1.2 0.3 1.06 9 13 #> log_lik[45] -1.0 -0.9 -0.9 -0.9 0.0 2.13 4 13 #> log_lik[46] -0.9 -0.9 -0.8 -0.9 0.0 1.20 5 13 #> log_lik[47] -1.5 -1.2 -1.0 -1.2 0.1 0.95 10 6 #> log_lik[48] -1.5 -1.3 -1.1 -1.3 0.2 1.18 9 13 #> log_lik[49] -2.2 -1.7 -1.5 -1.8 0.3 1.60 4 13 #> log_lik[50] -1.1 -0.9 -0.8 -1.0 0.1 1.27 5 13 #> log_lik[51] -1.5 -1.1 -0.8 -1.1 0.3 0.93 13 13 #> log_lik[52] -1.1 -1.1 -0.8 -1.0 0.1 1.16 5 4 #> log_lik[53] -1.8 -1.2 -1.0 -1.3 0.4 1.60 4 13 #> log_lik[54] -2.5 -1.8 -1.6 -1.9 0.3 1.39 5 6 #> log_lik[55] -3.3 -2.5 -1.3 -2.3 0.6 0.95 11 6 #> log_lik[56] -1.6 -1.2 -0.8 -1.2 0.3 1.06 11 13 #> log_lik[57] -0.9 -0.8 -0.8 -0.8 0.0 1.18 13 13 #> log_lik[58] -1.1 -0.9 -0.8 -0.9 0.1 1.27 5 13 #> log_lik[59] -1.1 -0.8 -0.8 -0.9 0.1 1.16 5 13 #> log_lik[60] -1.1 -0.9 -0.8 -0.9 0.1 2.11 4 13 #> log_lik[61] -1.3 -1.3 -1.3 -1.3 0.0 1.18 9 13 #> log_lik[62] -1.9 -1.3 -0.8 -1.4 0.4 1.50 4 13 #> log_lik[63] -1.4 -0.9 -0.8 -1.0 0.3 1.18 12 13 #> log_lik[64] -0.9 -0.8 -0.8 -0.9 0.0 1.41 7 4 #> log_lik[65] -0.8 -0.8 -0.8 -0.8 0.0 1.04 10 13 #> log_lik[66] -2.0 -1.6 -1.1 -1.6 0.3 1.26 5 13 #> log_lik[67] -0.9 -0.8 -0.8 -0.8 0.0 1.01 12 13 #> log_lik[68] -2.1 -1.8 -1.7 -1.9 0.2 1.48 12 13 #> log_lik[69] -1.7 -1.6 -1.4 -1.6 0.1 2.13 4 13 #> log_lik[70] -1.0 -0.9 -0.9 -0.9 0.0 1.19 7 6 #> log_lik[71] -2.1 -1.8 -1.5 -1.8 0.2 0.95 10 13 #> log_lik[72] -1.3 -1.1 -1.0 -1.1 0.1 1.35 9 6 #> log_lik[73] -3.8 -3.6 -3.6 -3.6 0.1 1.33 5 13 #> log_lik[74] -1.5 -1.2 -1.1 -1.3 0.1 1.26 5 13 #> log_lik[75] -2.4 -2.0 -1.9 -2.1 0.2 0.96 9 6 #> log_lik[76] -3.0 -2.7 -2.7 -2.8 0.1 0.93 11 13 #> log_lik[77] -2.4 -2.4 -2.2 -2.4 0.1 2.13 4 13 #> log_lik[78] -2.9 -2.2 -2.0 -2.3 0.3 1.26 5 13 #> log_lik[79] -1.0 -0.9 -0.8 -0.9 0.1 0.95 10 13 #> log_lik[80] -5.4 -5.2 -4.8 -5.2 0.2 0.93 11 4 #> xstar[1,1] -2.4 -0.6 1.0 -0.6 1.3 1.12 10 13 #> xstar[2,1] -0.3 0.5 1.5 0.5 0.7 1.37 6 13 #> sigma[1] 0.9 0.9 0.9 0.9 0.0 1.50 7 13 #> lp__ -105.9 -102.5 -102.3 -103.5 1.6 1.25 6 6 #> #> For each parameter, Bulk_ESS and Tail_ESS are crude measures of #> effective sample size for bulk and tail quantities respectively (an ESS > 100 #> per chain is considered good), and Rhat is the potential scale reduction #> factor on rank normalized split chains (at convergence, Rhat <= 1.05).
p <- plot_fitted(m) print(p)
p <- plot_fitted(m, spaghetti = TRUE) print(p)
# }